Evaluation of E-learning Platforms: a Case Study

Transcription

1 Informatca Economcă vol. 16, no. 1/ Evaluaton of E-learnng Platforms: a Case Study Crstna POP Academy of Economc Studes, Bucharest, Romana crstnel19@yahoo.com In the recent past, a great number of e-learnng platforms have been ntroduced on the market showng dfferent characterstcs and servces. These platforms can be evaluated usng multple crtera and methods. Ths paper proposes a lst of selected qualty crtera for descrbng, characterzng and selectng e-learnng platform. These crtera were desgned based on e-learnng standards. I also propose a mathematcal model to determne the probablty that a student uses an e-learnng platform based on the factors (crtera) that determne the qualty of the platform and the soco-demographc varables of the student. The case study presented s an applcaton of the model and the nput data, ntermedate calculatons and fnal results were processed usng SAS (Statstcal Analyss Software). Keywords: E-Learnng Platform, E-Learnng Standards, Learnng Object, Logstc Regresson, Qualty Crtera Lst, Unvarate Analyss 1 Introducton The World Wde Web s a repostory of content (fles, databases, datasets, mages, vdeo or audo clps, smulatons, anmatons, etc.) of all known formats and standards. The excessvely ncreasng load of nformaton on the Internet leads to an nevtable overload of useless nformaton or nformaton for commercalzaton purposes. Teachers and students may not use ths nformaton for ther educatonal need but rather as a global network for communcaton, nteracton and sharng. Wthn the onlne context, the user can be a content producer and consumer smultaneously [1], thus leadng to a huge amount of raw nformaton, produced by a huge number of heterogeneous users wthout any ddactc reformaton appled and ncapable to support classroom learnng desgn. In the educaton sector, there s always a qualty control procedure takng place aganst the educatonal materal of the schools from the Mnstry of Educaton. Therefore n the classcal meda context, s also need of multple crtera and methods to approve the qualty of e-learnng content and e-learnng software. 2 E-learnng platform Tradtonal means of learnng restrct the learner to certan learnng methods, at a specfc tme and place whereas e-learnng servces create wder horzons for organzatons and ndvduals who are nvolved n the learnng process. These envronments facltate the delvery of the learnng materals so the learner can access them at home or at the offce. The most part of contemporary e-learnng platform can be vewed as organzed nto three fundamental macro components: a Learnng Management System (LMS), a Learnng Content Management System (LCMS) and a Set of Tools for dstrbutng tranng contents and for provdng nteracton [2]. The LMS ntegrates all the aspects for managng on-lne teachng actvtes. The LCMS offers servces that allow managng content of the unts whle the Set of Tools represents all the servces that manage teachng processes and nteractons between users (students, teachers, admnstrators). An e-learnng platform can be characterzed through the followng management servces: servces for ncludng and updatng user profle; servces for creatng courses and catalogung them; servces for creatng tests descrbed through a standard; user trackng servces; servces for managng reports on course frequency and use;

2 156 Informatca Economcă vol. 16, no. 1/2012 servces for creatng, organzng and managng own tranng contents or contents provded by other producers [3]. 3 E-learnng standards Importance and need of specfcatons and standards are well known to all of us n dfferent areas of actvty. Standards mpose certan order provdng more unform and precse access and manpulaton to e-learnng resources and data. There are number of organzatons workng to develop specfcatons and standards such as: ADL, IMS, ARIADNE, IEEE, ISO etc to provde framework for e-learnng archtectures, to facltate nteroperablty, content packagng, content management, Learnng Object Metadata, course sequencng and many more [4]. The ADL (Advanced Dstrbuted Learnng) ntatve s to provde access to the hghest qualty learnng and performance adng that can be talored to ndvdual needs, and delvered cost effectvely at the rght tme and at the rght place [5]. The ADL s accountable for the Sharable Content Object Reference Model (SCORM), a well-known and accepted standard for all users of e- learnng platforms. Ths standard conssts of three separate specfcatons: Content Aggregaton Model (CAM) for assemblng, labelng, and packagng of learnng content. The basc unts of nterest n the Content Aggregaton Model are Sharable Content Objects (SCO) and Content Packages that are used to delver content Run-Tme Envronment (RTE) whch ncludes Launch (descrbes how a LMS provdes Content Packages to the learner), Applcaton Programmng Interface (communcaton nterface between Content Packages and LMS durng executon) and Data Model (LMS records the result of nteracton between learner and learnng object usng data model). Sequencng and Navgaton (SN) for sequencng and content navgaton. Ths module controls and montors the nteracton between users and LMS. These specfcatons are based on IMS Consortum specfcatons. Instructonal Managements Systems (IMS) Global Learnng Consortum s a consortum of e-learnng solutons provders. The standard IMS focuses on the development of XML-based specfcatons. Several IMS specfcatons have become worldwde standards for delverng learnng products and servces: IMS Content Packagng specfcaton descrbes data structures that can be used to exchange data between systems that wsh to mport, export, aggregate, and dsaggregate packages of content [6]; IMS Learnng Desgn specfcaton allows a wde range of teachng technques n onlne learnng; IMS Meta-data specfcaton descrbes a learnng object and allows to specfy an annotaton to search these educatonal resources effcently; IMS Queston and Test Interoperablty descrbes a standard data model for representng the test tems and reports evaluaton results; IMS Learner Informaton Package s a collecton of nformaton about the learner (ndvdual or group learners) or the producer of learnng content (teachers or provders); IMS eportfolo specfcaton was created to make eportfolos nteroperable across dfferent systems and nsttutons. Allance of Remote Instructonal Authorng and Dstrbuton Networks for Europe (ARIADNE) has created a standards-based technology nfrastructure that allows the publcaton and management of dgtal learnng resources n an open and scalable way. ARIADNE ams to provde flexble, effectve and effcent access to large-scale educatonal collectons n a way that goes beyond what typcal search engnes provde[7]. IEEE Learnng Technology Standards Commttee (LTSC) s chartered by the IEEE Computer Socety Standards Actvty Board to develop accredted techncal standards, recommended practces, and gudes for

3 Informatca Economcă vol. 16, no. 1/ learnng technology [8]. The IEEE/LTSC s organzed n workng groups to develop dfferent aspects of learnng technology. Internatonal Standardzaton Organzaton (ISO). A subcommttee of the worldwde operatng standardzaton body ISO, the JTC1/SC 36 commttee, s workng on standardzaton ssues n nformaton technology for learnng, educaton and tranng n lason wth the IEEE/LTSC. The ISO/JTC1/SC36 commttee s organzed nto fve workgroups on: vocabulary; collaboratve technology; learner nformaton; management and delvery of learnng, educaton, and tranng; qualty assurance and descrptve frameworks [9]. I would also lke to propose several specfcatons for the qualty of e-learnng content (Learnng Object, LO): 1. LO objectves at the begnnng of each LO teacher should clearly defne the objectves, so the students should be aware of what they learn. 2. LO should be desgned by level of dffculty the students have not the same level of understandng, therefore teachers should desgn LO by level of dffculty (very advanced, advanced, average, begnner). 3. LO should be completed wthn a certan tme (.e. from 5 to 15 mnutes) the content of the LO should be lmted to a certan perod of tme so students do not get bored. 4. Glossary new terms should have a bref explanaton n the glossary of each LO 5. Recaptulaton and summary at the begnnng of each LO should be a presentaton (recaptulaton) of the concepts that should be known for a better understandng of the new content. At the end of the LO should be a summary of the learnng content. Student may choose whether to read the entre content of the LO or just the summary. 6. Detaled feedback on learnng progress - student should revew certan chapters, paragraphs, etc.; teacher should hghlght the postve aspects; student should access external lnks for more nformaton. 4 Qualty crtera lst The growng number of avalable e-learnng systems and the commercalzaton of these systems hghlght the necessty of qualty evaluatons of onlne publshed learnng materals. Although qualty evaluaton of learnng materals n e-learnng systems have become ncreasngly mportant, the actual evaluaton standards and methods for nformaton qualty (IQ) n such systems have not yet reached a consensus [10]. The evaluaton of e-learnng systems s mportant for all the actors nvolved n the learnng process. Teachers and students need to evaluate the benefts of usng e-learnng n comparson wth the classcal methods of learnng [11]. Evaluaton of e-learnng platforms requres evaluatng not only the mplementng software package (Learnng Management System), but also the e-learnng content (Learnng Object). Both pedagogcal and technologcal aspects must be carefully evaluated. The followng qualty crtera were developed based on the e-learnng standards (.e. Scorm, Learnng Object Metadata, IMS Specfcatons, etc.). I outlne below sx basc categores for the evaluaton of the Learnng Management System (functonalty, communcaton/ collaboraton, accessblty/effectveness, management of e-learnng content and users, admnstraton, tools and technology) and others sx categores for the evaluaton of the Learnng Objects (ddactc and pedagogcal evaluaton, metadata, content evaluaton, multmeda presentaton, evaluaton of the users, technology).

4 158 Informatca Economcă vol. 16, no. 1/2012 Table 1. Qualty Crtera Lst Learnng Management System Learnng Object (LO) A. Functonalty A. Ddactc and Pedagogcal Evaluaton A.1 Sequencng and Navgaton Structure A.1.1 Paragraphs A.1.2 Menus A.1.3 External Lnks A.1.4 Stemap A.1 LO should be desgn on dfferent levels of dffculty (very advanced, advanced, average, begnner) A.2 LO for dfferent learnng profle A.3 LO should be completed wthn a certan tme (.e. from 5 to 15 mnutes) A.4 LO objectves A.5 Recaptulaton LO A.1.5 Search Engne A.6 Summary LO A.1.6 Smart Navgaton B. Communcaton/Collaboraton B. Learnng Object Metadata [12] B.1 Emal B.1 General (.e. ttle, descrpton, keyword) B.2 Forum B.2 Lfe Cycle (.e. verson, status) B.3 Chat B.3 Meta-Metadata (.e. dentfer, metadata schema) B.4 Web-blog B.4 Techncal (.e. format, sze, locaton) B.5 Wk B.5 Educatonal (.e. nteractvty type, learnng resource type, nteractvty level) B.6 Whteboard B.6 Rghts (.e. cost, copyrght, descrpton) B.7 Relaton (.e. knd, resource) B.8 Annotaton (.e. entty, date, descrpton) B.9 Classfcaton (.e. purpose, descrpton, keyword) C. Accessblty/Effectveness C. Evaluaton of the LO content C.1 Access Status (free, payment, C.1 Free-of-error mxed) C.2 Multlngual Content C.2 Relevance C.3 Complance to W3CWAI C.3 Accessblty Standards C.4 Plug-ns needed C.4 Credblty/Valdty C.5 Users feedback for evaluaton of e-learnng platform D. Management of e-learnng content and users C.5 Updated C.6 Easy of manpulaton D. Multmeda presentaton D.1 Progress report for users D.1 Balance between textual and vsual elements D.2 Grade book D.2 Attractve content presentaton D.3 Progress report for Learnng D.3 Entertanment games

5 Informatca Economcă vol. 16, no. 1/ Learnng Management System Learnng Object (LO) Object D.4 Export reports (.e. Excel, PDF) D.4 Educatonal games E. Admnstraton E. LO for evaluaton E.1 User regstraton E.1 Dfferent tems for evaluaton (.e. multple choce, true/false, free text, empty spaces, drag and drop-matches) E.1.1 Students E.2 Intal evaluaton (before the learnng process) E.1.2 Teachers E.3 Fnal evaluaton (at the end of the learnng process) E.1.3 Admnstrator E.1.4 Other users (.e. parents) E.2 Templates for dfferent user nterface E.4 Feedback on learnng progress E.4.1 Students should revew certan chapters, paragraphs, etc. E.4.2 Teachers should hghlght the postve aspects E.3 System settngs E.4.3 Students should access external lnks for more nformaton E.4 Management of user groups E.5 Backup System E.6 System Mantenance E.7 Other modules F. Tools and Technology F. LO Technology F.1 The e-learnng platform can be access by a standard browser (the browser dsplays all the multmeda content) F.1 Reusablty - a sngle LO may be used n multple contexts for multple purposes F.2 Frendly user nterface F.2 Interoperablty - LO may be used by dfferent e-learnng platforms F.3 Download speed of large nformaton F.3 LO can be aggregated LO can be grouped nto larger collectons of content, ncludng tradtonal course structures F.4 Techncal characterstcs F.4 LO are self-contaned each LO can be taken ndependently 5 The mathematcal model used for the evaluaton of e-learnng platforms The evaluaton process conssted of the followng steps: Constructon of the sample (sample requrements, model performance, model development); Fne classng and unvarate analyss of data; Multvarate analyss lnear regresson and logstc regresson; Correlaton analyss; Valdaton of the model. 5.1 Constructon of the sample Varable whose value I wsh to predct s called the crteron or the dependent varable and the varable whose value s used to predct the crteron s called the predctor or

6 160 Informatca Economcă vol. 16, no. 1/2012 the ndependent varable. In ths case, the crteron varable s: usng an e-learnng platform to meet certan qualty crtera s enough for better understandng, learnng and assessment knowledge and the predctor varables are the qualty crtera lst (descrbed n table 1) and soco-demographc characterstcs of the student. I used a survey to dentfy the tranng needs of the users. Example of queston n the survey: Do you consder the user s feedback mportant for the evaluaton of an e-learnng platforms? Users may answer: Yes, I agree; No, I dsagree; I don t know. I say they are good those who answer yes, I agree, bad those who answer no, I dsagree, and ndetermnate for those who are undecded. The goal s to buld a model to dscrmnate between good and bad. Group Good Bad Indetermnate Table 2. GB classfcaton Defnton Yes, I agree No, I dsagree Other response Sample requrements: Qute recently, n order to resemble wth a real stuaton; Representatve for the target populaton; To contan a suffcent number of bad, a mnmum of 4% Model performance: the event to be predcted s the probablty that an user s answer s good. It s necessary to exclude all those undecded, for a good dscrmnaton between good and bad. Development and Hold-out sample: The database wll be dvded nto two, respectng the orgnal proportons (weghts 70% - 30% or 80% - 20%): The base development, used for the model development; The base used for the valdaton of the model. 5.2 Fne classng and unvarate analyss of data Conssts n amalgamatng observatons nto a set of ranges or ntervals to produce statstcs (e.g. good/bad odds) that could not be produced for ndvdual observatons (as one observaton s ether good or bad). It s these ntervals that undergo analyss and from whch nferences can be drawn about the mportance of a characterstc n the development. There are many methods to determne an optmum number of ntervals (e.g. Sturges method), but I consder enough that each nterval to contan about 5% - 10 % from the base. Non-numerc varables wll be grouped separately and analyzed n the same manner (e.g. gender, year of study, job, etc.). The purpose of the unvarate analyss s to dentfy all the varables that can be consdered as sutable predctors of the probablty of a student beng Good. I calculate WoE (Weght of Evdence) whch ndcates that t s necessary to group multple ranges nto one. % good WOE ln % bad A method of excludng varable that s not representatve s gven by Informaton Value, IV. IV (% good % bad) * WOE k k k represents number of groups.

7 Informatca Economcă vol. 16, no. 1/ Table 3. Measures of explanatory power Power of explanaton Informaton Value Gn Index Low <0.02 <10% Medum 0.02 to % to 20% Good 0.1 to 1 20% to 30% Very good > 1 > 30% Gn Index s calculated by comparng the cumulatve number of goods and bads by score. Graphcally, t s the area between the two lnes on the curve (XYW) expressed as a percentage of the maxmum possble (XYZ). The two axes on the graph are cumulatve percentage of goods (y-axs) and cumulatve percentage of bads (x-axs). Y Cum % Goods W X Cum % Bads Fg. 1. Gn Index Z Gn Index s calculated as follow: g = cumulatve percentage of good at a gven score; b = cumulatve percentage of bad at a gven score; S n = the n-th score n the score dstrbuton. Usng smple geometry, the area under the curve for a gven score s defned as: 1 A score ( b b 1)*( g g1) 2 The total area of (XYZ) mnus the total area of (XYW) s: S n A g A S2 The area of trangle (XYZ) s equal to: 1 A T (100*100) 5,000 2 The Gn coeffcent s then calculated as the modulus of: ( AT Ag ) g AT The result s between 0 and 1, as a proporton. The Informaton Value measure s calculated as follows: n g b g. B I.log 1 G B b. G where G and B are the total number of good and bad respectvely 5.3 Multvarate analyss lnear regresson and logstc regresson Generalzng, the term Regresson s used to characterze the way n whch the measurement of an unobserved (or dependent) varable Y changes accordng to the measurements of one or more dfferent events (the ndependent varables x, =1, 2, ). The purpose of a regresson analyss s

8 162 Informatca Economcă vol. 16, no. 1/2012 to quantfy the relatonshp between the dependent and ndependent varables. Lnear regresson: n lnear regresson the objectve s to fnd an equaton that lnks the latter to the former through a lnear functon: Y X n X n The coeffcents represent the weghts to apply to the value of the ndependent varables to estmate the dependent varable Y; the term s the error term, the dfference between the actual and the predcted values of Y. The coeffcents are determned so as to mnmze the sum of the squared errors (Ordnary Least Squares crteron), but there are some other robust methods n presence of outlers n data. Logstc regresson n logstc regresson the unobserved varable Y s a Bernoullan random varable whose possble values are 0 and 1. The probablty that Y can assume the value 1 depends on the regressors set x ( 1,2,..., n) : P( Y 1 X x) ( x),(1) The procedure for estmatng such a probablty s based on the comparson (odds rato) between the probablty of an event happenng and the probablty that t does not happen: P( Y 1 X x) P( Y 1 X x) ( x) odds( x),(2) P( Y 0 X x) 1 P( Y 1 X x) 1 ( x) The natural logarthm of the odds (logt) s a lnear functon of the regressors x : ln odds( x) x x... x,(3) Combnng formulas (2) and (3) and solvng by (x), the logstc functon of probablty estmaton that the event happens s: 0 1x1... nxn e ( x),(4) 0 1x1... nxn 1 e The logstc regresson makes use of maxmum lkelhood estmaton methods for estmatng the regressors. The parameters are estmated usng Maxmum-Lkelhood Estmaton. Maxmum lkelhood functon s: 1 y y L ( x ) 1 ( x ),(5) n n Wald Test s used to test the statstcal sgnfcance of each coeffcent n the model. Ths test s equvalent to T -Test used n lnear regresson. When the null hypothess s rejected, I assume that the estmated parameter s sgnfcant (non zero), therefore p-estmated s below 5%: ˆ p estmat 2 probnorm, ˆ - S estmaton of and ˆ S ˆ - ts dsperson (calculated as the root of dagonal covarance matrx) Usng SAS, all these statstcs wll be done usng the procedure proc logstc and backward method. 5.4 Correlaton analyss Correlaton ndcates the strength and drecton of a lnear relatonshp between two varables. It s good practce to produce a correlaton matrx that contans the correlaton between each varable consdered n the analyss. The analyss of the correlaton matrx wll often reveal why a varable that appeared to have consderable explanatory power (as revealed by the unvarate analyss) was not selected by the backward procedure. If two (or more) varables are extremely hghly correlated n fact, t s unlkely that they all end up n the fnal model. If there are reasons to prefer one of the excluded varables t s possble to run agan the regresson analyss removng one or more varables correlated to t (ths s somewhat a tral and error procedure). Correlaton analyss s also necessary to make sure that all the varables that enter the model are uncorrelated so as to grant parameters statstcal robustness. Although, as explaned above, the backward procedure results generally n a model that does not nclude varables wth a hgh degree of correlaton, a vsual nspecton of the correlaton matrx s stll necessary ensure that ths s the case (correlaton s more common n behavor and collecton models). In analyzng the correlaton matrx, values

9 Informatca Economcă vol. 16, no. 1/ hgher than can be consdered as ndcatng a sgnfcant correlaton between two varables. Regresson s a repettve process that wll take place untl the nput varables wll be retaned n the model and there wll be no exclusons. Valdaton of ndvdual parameters wll be done usng Wald Test. Logcal trend even f a varable has a sgnfcant power, I need to follow f the output s logc. If the analyss was properly performed, the model should be predctve and mathematcally correct. Obvously Weght of Evdence should follow a lnear upward trend and the results (weghts or estmated regresson parameters) obtaned for each nterval wll be constructed to have a smlar logc. The lower class wll get the lowest score. A partcular attenton should be gven to the sgn of coeffcents. For example, gnorng the rest of varable, f GB odds s subunt then the logarthm of the odds s negatve and I expect that the sgn of estmated regresson ntercept s negatve. 5.5 Valdaton of the model To provde a hgh level revew of the model performance, you should examne the score dstrbuton, then the Good/Bad odds and bad rate by score-band n order to ensure the model dsplays the expected performance. All shfts and problems should be nvestgated. The dscrmnatory power of a model s a measure of ts ablty to forecast whether a borrower wll default or not (ex-ante). Ths dscrmnatory power can be assessed usng a number of statstcal measures of dscrmnaton such as the Kolmogorov- Smrnov (KS) statstc or Gn coeffcent. The KS statstc s used to assess the model performance by measurng the maxmum dvergence between cumulatve goods and cumulatve bads at each score or score-band. Another tool used to assess model performance s the effcency curve or ROC (Recever Operatng Characterstc) curve. The ROC chart s used to assess the predctve power of the scorecard across all score ranges by lookng at actual dscrmnaton compared to perfect dscrmnaton. The Gn coeffcent s the area under the ROC curve (measured as a percentage). The hgher the Gn the stronger s the dscrmnaton of the scorecard. A scorecard wth no dscrmnaton would have a Gn of zero; a perfect scorecard would have a Gn of 100%. The KS and Gn measures can be assessed accordng to the followng broad gudelnes for applcaton and behavor scorecards. Table 4. Gudelnes for KS and Gn Index Power of dscrmnaton Kolmogorov-Smrnov statstc Gn Index Low <30% <40% Medum 30% to 45% 40% to 55% Hgh > 45% > 55% These values must be consdered smlar to the valdaton sample, after ts calculaton wth the parameters obtaned from the regresson model on development data. 5.6 The case study: statstc summary Constructon of the sample: the sample used n the model was chosen randomly, wth the 1,000 respondents aged between 14 and 40 years old. Descrpton of sgnfcant varables: n the prelmnary analyss I excluded the correlated varables and I kept those wth hgher Informaton Value. These varables are descrbed n the Defnton column from the next table. Easer to use n the process modelng, I have renamed them as descrbed n the column Name :

10 164 Informatca Economcă vol. 16, no. 1/2012 Table 5. The lst of sgnfcant varables Statstcal Name Type Type Defnton V1 Numerc Metrc Knowledge volume/year on the platform V2 Numerc Metrc How many levels of tranng do you consder necessary the classfcaton of learnng objects (.e. begnner/medum/advanced)? V3 Numerc Metrc How many seconds s reasonable to download a page even f a large number of users are smultaneously connected to the platform? V4 Numerc Metrc Durng your teachng/learnng actvty how many hours/day do you use addtonal resources of nformaton and nternet? V5 Numerc Metrc Age V6 Numerc Metrc The number of mnutes/day usng the platform V7 Numerc Metrc How many nternatonal languages do you consder necessary to use the platform? V8 Numerc Metrc Year of study V9 Character Categorcal Do you consder necessary that each user to receve a certan educatonal materal dependng on hs learnng style? V10 Character Categorcal Usng an e-learnng platform, do you consder necessary to communcate wth the teacher and/or other users (.e. emal, forum, chat, blog, etc.)? V11 Character Categorcal Educaton V12 Character Categorcal Gender V13 Character Categorcal Do you consder that the evaluaton feedback has to be very detaled (.e. explanaton of ncorrect answers, hghlghtng the correct answers, scorng procedures, ndcatng pages and sectons that need to be revewed, recommendng addtonal materals for a better understandng of concepts/terms)? V14 Character Categorcal Usng an e-learnng platform, do you consder necessary to rank the educatonal materals (.e. module/course/chapter)? V15 Character Categorcal Dscplne of study V16 Character Categorcal Do you consder the user s feedback mportant for the evaluaton of an e-learnng platforms? V17 Character Categorcal Do you need to mport/export learnng objects n SCORM format/ims Content Packagng or another format? Table 6. Varables selecton Item p-value Gn Index Reason of keepng/ excluson V1 < No addtonal nformaton V No addtonal nformaton V3 < OK V4 < No addtonal nformaton V5 < OK V6 < OK V7 < No addtonal nformaton V8 < No addtonal nformaton V9 < OK

11 Informatca Economcă vol. 16, no. 1/ Item p-value Gn Index Reason of keepng/ excluson V Wanted t n the model V11 < OK V12 < No addtonal nformaton V13 < OK V14 < OK V15 < No addtonal nformaton V16 < No addtonal nformaton V17 < No addtonal nformaton After data processng, the followng varables were consdered representatve: Table 7. Representatve varables Name KS statstc p-value V <.0001 V <.0001 V <.0001 V <.0001 V V V <.0001 V <.0001 Correlaton analyss: For correlaton analyss I consdered WoE/group. I preferred Spearman correlaton coeffcent (rank) because t provdes robust results for ths outler n the data. Table 8. Correlaton analyss WOE_V WOE_V WOE_V WOE_V WOE_V WOE_V WOE_V WOE_V Next, I wll present only the results for varable V13 = Feedback, to observe the logcal trend for BR (Bad Rate) and WoE. Item Defnton Transformaton Table 9. Results for V13 V13 Do you consder that the evaluaton feedback has to be very detaled? WOE Informaton value 0.06

12 166 Informatca Economcă vol. 16, no. 1/2012 Table 10. BR and WoE for V13 Group Groupng #Bad #Good Total BR WOE 1 A,B % C,D % E,F,G,H % I % Fg. 2. Logcal trend for BR and WoE for V13 Bad Rate (BR) WOE Logcal trend: WoE, bult for each group, has a lnear upward trend, from the weakest to the most valuable group, whle BR has a downward trend. 6 Conclusons In order to accurately evaluate the possbltes of an e-learnng platform, t s mportant to pay attenton to the Learnng Management System (LMS) and the Learnng Objects (LO). These two components have to meet certan qualty crtera based on e-learnng standards. An effcent e-learnng system must be able to meet these qualty crtera. Of course that wth the development of new standards, qualty crtera lst should be updated. The proposed mathematcal model determne the probablty that a student uses an e-learnng platform based on the factors that determne the qualty of the platform (the tme to download a page even f a large number of users are smultaneously connected to the platform, tools for communcaton wth the teacher and/or other users, adaptng educatonal materal to each user s learnng style, herarchy of the educatonal materals and the complexty of the evaluaton feedback) and the soco-demographc varables of the student (educaton, age, the average tme a student uses a platform). Ths model may be used n two dfferent stuatons, as follows: Case 1: To evaluate two dfferent e-learnng platforms (platform A and platform B) for students wth the same profle. It establshes a student profle (.e. students aged 20 usng a platform an average of 30 mnutes/day for ther learnng actvty) and characterstcs of two dfferent platforms (the tme to download a page even f a large number of users are smultaneously connected to the platform, tools for communcaton wth the teacher and/or other users, adaptng educatonal materal to each user s learnng style, herarchy of the educatonal materals and the complexty of the evaluaton feedback). Usng the regresson model I determne the probablty that the students use the platform A and the probablty that the students use the platform B. The platform

13 Informatca Economcă vol. 16, no. 1/ that wll acheve the greatest probablty, s more approprate for ths student profle. Case 2: In ths stuaton one platform s evaluated for dfferent student profle (.e. hgh school graduates aged 19 usng a platform 30 mnutes/day and PhD aged 30 usng a platform 30 mnutes/day). In ths case the characterstcs of the platform (the tme to download a page even f a large number of users are smultaneously connected to the platform, tools for communcaton wth the teacher and/or other users, adaptng educatonal materal to each user s learnng style, herarchy of the educatonal materals and the complexty of the evaluaton feedback) are the same but the student profle s dfferent. Usng the regresson model I determne the probablty that each student uses the platform. If the determned probablty s hgher for PhD student then the platform s more useful for ths student profle. References [1] S. Alvzos, K. Apostolas, Pedagogcally-Orented Evaluaton Crtera for Educatonal Web Resources, elearnng Papers, No. 17, December 2009, ISSN , Avalable at: [2] F. Colace, M. De Santo, M. Vento, Evaluatng On-lne Learnng Platforms: a Case Study, Proceedngs of the 36 th Hawa Internatonal Conference on System Scences (HICSS 03), [3] F. Colace, M. De Santo, Evaluaton models for e-learnng platforms and the AHP approach: a case study, [4] S. Chandra Babu, e-learnng Standards, Avalable at: sesson6.1.pdf [5] ADL (Advanced Dstrbuted Learnng Intatve), ADL Overvew, Avalable at: [6] Content Packagng Specfcaton, Avalable at: content/packagng/ [7] ARIADNE (Allance of Remote Instructonal Authorng and Dstrbuton Networks for Europe), Avalable at: [8] IEEE LTSC (Insttute of Electrcal and Electroncs Engneers) IEEE LTSC Avalable at: [9] I. A. Uţă, E-learnng Standards, Informatca Economca Journal, No. 1 (41), 2007, Avalable at: 0Uta.pdf [10] M. Alkhattab, D. Neagu, A. Cullen, Assessng nformaton qualty of e- learnng systems: a web mnng approach, Computers n Human Behavor, [11] V. Posea, S. Matu and V. Crstea, Onlne Evaluaton of Collaboratve Learnng Platforms, Poltehnca Unversty of Bucharest, Computer Scence Department, Bucharest, Romana, [12] WhatIsLOM.pdf. Crstna POP has graduated the Faculty of Scence, Unversty Lucan Blaga Sbu, n She was a math teacher for two years n South Carolna, USA. Currently she s teachng computer scence at Colegul Tehnc de Transportur, Brasov and she s PhD canddate to Economc Informatcs Department, Academy of Economc Study, Bucharest, Romana. Her work focuses on the evaluaton of e-learnng platforms.